There is growing interest in exploring unsupervised learning of VO.
Unsupervised solutions are capable of exploiting unlabelled sensor data, and thus it saves human effort on labelling data, and has better adaptation and generalization ability in new scenarios, where no labelled data are available.
This has been achieved in a self-supervised framework that jointly learns depth and camera ego-motion from video sequences, by utilizing view synthesis as a supervisory signal[1]}.
